Pipeline run
807865a4-800e-4baf-94b2-163ee8f95755
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
Software Engineering and Product development is critical to the success of IBM and our clients worldwide. You will collaborate with top industry thinkers at IBM and utilise the newest software develop…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
NoSQL DBA / Engineer
domain · Database Specialists CASE DOMAINslug: nosql-dba-engineer · id: 200 · source: db
Domain=Database Specialists; The JD focuses on designing, implementing, and optimizing vector database systems with technologies like Milvus, Elasticsearch, and hybrid search, which best matches a NoSQL / modern database engineering role.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
674679BR Introduction At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk. Your Role and Responsibilities Software Engineering and Product development is critical to the success of IBM and our clients worldwide. You will collaborate with top industry thinkers at IBM and utilise the newest software development tools,methodologies, and techniques to create solutions you can be proud of. We are seeking seasoned people with a strong interest in data and AI to join our watsonx.data engineering team. We are seeking professionals who are knowledgeable about the latest offerings in data warehouse, data lake, and data lakehouse technologies. Professionals who are already involved in these products' open source communities or who actively monitorthem will have an edge during the hiring process. As a Vector Database Engineer, you will play a crucial role in designing, implementing, and maintaining our vector database systems. You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data. The ideal candidate will have a strong background in database architecture, optimization, and a deep understanding of vector data structures. • You will work in an environment that is driven by innovation and collaboration to comprehend requirements, architect, design, and implement functionalities • You will collaborate with customers to translate their needs into product features. • You will work in an agile development environment and participate in large-scale scrums. • You will actively contribute to all stages of software development, from requirements grooming to development and testing. • You will experience a culture of continuous learning to support your professional growth. • You will create proof of concepts to establish technical viability. • You will be able to mentor junior team members (for senior bands). • You will have the chance to work with a variety of open source products and actively contribute back to their communities. • You will have equal opportunities to work on cloud and on-prem systems. Required Technical and Professional Expertise • Overall 3+ years of IT Industry experience • 2+ years of with OpenAI Apis/Opensource models • Bachelor's or higher degree in Computer Science, Information Technology, or a related field. • Proven experience as a Database Engineer, with a focus on vector databases. • Strong proficiency in database design, optimization, and performance tuning. • Experience with vector data structures and algorithms. • Proficiency in programming languages such as Python, Java, or C++. • Knowledge of database technologies such as Mivus or similar DB. • Experience on hybrid search techniques such as SPLADE V2 • Excellent problem-solving and analytical skills. • Strong communication and collaboration skills. Preferred Technical And Professional Expertise • 2+ years of with OpenAI Apis/Opensource models • Familiarity with HuggingFace • Familiarity with Elasticsearch About Business Unit IBM Software infuses core business operations with intelligence—from machine learning to generative AI—to help make organizations more responsive, productive, and resilient. IBM Software helps clients put AI into action now to create real value with trust, speed, and confidence across digital labor, IT automation, application modernization, security, and sustainability. Critical to this is the ability to make use of all data, because AI is only as good as the data that fuels it. In most organizations data is spread across multiple clouds, on premises, in private datacenters, and at the edge. IBM’s AI and data platform scales and accelerates the impact of AI with trusted data, and provides leading capabilities to train, tune and deploy AI across business. IBM’s hybrid cloud platform is one of the most comprehensive and consistent approach to development, security, and operations across hybrid environments—a flexible foundation for leveraging data, wherever it resides, to extend AI deep into a business. This job requires you to be fully COVID-19 vaccinated prior to your start date and proof of vaccination status will be required before your start date. During the Onboarding process you will be asked to confirm your vaccination status, in case you are unable to get vaccinated for any reason, you can let us know at that stage. Please let us know if you are unable to be vaccinated due to medical or religious reasons. IBM will consider such requests on a case by case basis subject to submission of required proof by the candidate before a stipulated date. Your Life @ IBM In a world where technology never stands still, we understand that, dedication to our clients success, innovation that matters, and trust and personal responsibility in all our relationships, lives in what we do as IBMers as we strive to be the catalyst that makes the world work better. Being an IBMer means you’ll be able to learn and develop yourself and your career, you’ll be encouraged to be courageous and experiment everyday, all whilst having continuous trust and support in an environment where everyone can thrive whatever their personal or professional background. Our IBMers are growth minded, always staying curious, open to feedback and learning new information and skills to constantly transform themselves and our company. They are trusted to provide on-going feedback to help other IBMers grow, as well as collaborate with colleagues keeping in mind a team focused approach to include different perspectives to drive exceptional outcomes for our customers. The courage our IBMers have to make critical decisions everyday is essential to IBM becoming the catalyst for progress, always embracing challenges with resources they have to hand, a can-do attitude and always striving for an outcome focused approach within everything that they do. Are you ready to be an IBMer? About IBM IBM’s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world.Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we’re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business. At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Now it’s time for you to join us on our journey to being a responsible technology innovator and a force for good in the world. Location Statement When applying to jobs of your interest, we recommend that you do so for those that match your experience and expertise. Our recruiters advise that you apply to not more than 3 roles in a year for the best candidate experience. For additional information about location requirements, please discuss with the recruiter following submission of your application. Being You @ IBM IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- OpenAI API (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Llm Api Service
- Vendor
- OpenAI
- License
- other_open
- Year introduced
- 2020
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Strong JD signal: OpenAI API is now commonly listed in AI/ML and full-stack roles, and OpenAI’s own platform docs and ecosystem integrations show broad production adoption.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 1006
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Provider APIs Catalog dimension db id 195
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Provider APIs
llm-provider-apis
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Java (CANONICAL) primary
- JDK (VERSION)
- JDK 10 (VERSION)
- JDK 11 (VERSION)
- JDK 12 (VERSION)
- JDK 13 (VERSION)
- JDK 14 (VERSION)
- JDK 15 (VERSION)
- JDK 16 (VERSION)
- JDK 17 (VERSION)
- JDK 18 (VERSION)
- JDK 19 (VERSION)
- JDK 20 (VERSION)
- JDK 21 (VERSION)
- JDK 5 (VERSION)
- JDK 6 (VERSION)
- JDK 7 (VERSION)
- JDK 8 (VERSION)
- JDK 9 (VERSION)
- Java 1.0 (VERSION)
- Java 1.1 (VERSION)
- Java 1.2 (VERSION)
- Java 1.3 (VERSION)
- Java 1.4 (VERSION)
- Java 1.5 (VERSION)
- Java 1.6 (VERSION)
- Java 1.7 (VERSION)
- Java 1.8 (VERSION)
- Java 10 (VERSION)
- Java 11 (VERSION)
- Java 12 (VERSION)
- Java 13 (VERSION)
- Java 14 (VERSION)
- Java 15 (VERSION)
- Java 16 (VERSION)
- Java 17 (VERSION)
- Java 18 (VERSION)
- Java 19 (VERSION)
- Java 20 (VERSION)
- Java 21 (VERSION)
- Java 5 (VERSION)
- Java 6 (VERSION)
- Java 7 (VERSION)
- Java 8 (VERSION)
- Java 9 (VERSION)
- Java11 (VERSION)
- Java17 (VERSION)
- Java21 (VERSION)
- Java8 (VERSION)
- OpenJDK 11 (VERSION)
- OpenJDK 17 (VERSION)
- OpenJDK 21 (VERSION)
- OpenJDK 8 (VERSION)
- java 11 (VERSION)
- java 17 (VERSION)
- java 21 (VERSION)
- java 4 (VERSION)
- java 5 (VERSION)
- java 6 (VERSION)
- java 7 (VERSION)
- java 8 (VERSION)
- java lts (VERSION)
- java-11 (VERSION)
- java-17 (VERSION)
- java-21 (VERSION)
- java-4 (VERSION)
- java-5 (VERSION)
- java-6 (VERSION)
- java-7 (VERSION)
- java-8 (VERSION)
- java11 (VERSION)
- java17 (VERSION)
- java21 (VERSION)
- java4 (VERSION)
- java5 (VERSION)
- java6 (VERSION)
- java7 (VERSION)
- java8 (VERSION)
- jdk 11 (VERSION)
- jdk 17 (VERSION)
- jdk 21 (VERSION)
- jdk 4 (VERSION)
- jdk 5 (VERSION)
- jdk 6 (VERSION)
- jdk 7 (VERSION)
- jdk 8 (VERSION)
- jdk11 (VERSION)
- jdk17 (VERSION)
- jdk21 (VERSION)
- jdk4 (VERSION)
- jdk5 (VERSION)
- jdk6 (VERSION)
- jdk7 (VERSION)
- jdk8 (VERSION)
- jvm21 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Oracle
- License
- other_open
- Year introduced
- 1995
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 21
Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Java Language and JVM Catalog dimension db id 279
Library dimension (catalog)
Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer
-
Kotlin and Java Catalog dimension db id 161
Library dimension (catalog)
Roles linked in library: Android Developer
-
Native Mobile Languages Catalog dimension db id 274
Library dimension (catalog)
Roles linked in library: Native Mobile Developer
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- C# (CANONICAL) primary
- C (CANONICAL)
- C# 1 (VERSION)
- C# 10 (VERSION)
- C# 11 (VERSION)
- C# 12 (VERSION)
- C# 13 (VERSION)
- C# 14 (VERSION)
- C# 2 (VERSION)
- C# 3 (VERSION)
- C# 4 (VERSION)
- C# 5 (VERSION)
- C# 6 (VERSION)
- C# 7 (VERSION)
- C# 8 (VERSION)
- C# 9 (VERSION)
- C# latest (VERSION)
- C#1 (VERSION)
- C#10 (VERSION)
- C#11 (VERSION)
- C#12 (VERSION)
- C#2 (VERSION)
- C#3 (VERSION)
- C#4 (VERSION)
- C#5 (VERSION)
- C#6 (VERSION)
- C#7 (VERSION)
- C#8 (VERSION)
- C#9 (VERSION)
- C++ (CANONICAL)
- C++03 (VERSION)
- C++11 (VERSION)
- C++14 (VERSION)
- C++17 (VERSION)
- C++20 (VERSION)
- C++23 (VERSION)
- C++26 (VERSION)
- C++98 (VERSION)
- c sharp (VERSION)
- c# (VERSION)
- cpp03 (VERSION)
- cpp11 (VERSION)
- cpp14 (VERSION)
- cpp17 (VERSION)
- cpp20 (VERSION)
- cpp23 (VERSION)
- cpp26 (VERSION)
- cpp98 (VERSION)
- csharp (VERSION)
- modern C++ (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2000
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- latest
Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
C# and .NET Languages Catalog dimension db id 362
Library dimension (catalog)
Roles linked in library: .NET Backend Developer
-
Cross-Platform App Languages Catalog dimension db id 167
Library dimension (catalog)
Roles linked in library: Hybrid Mobile Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Sitecore Development Languages Catalog dimension db id 438
Library dimension (catalog)
Roles linked in library: Sitecore Dev
-
Video Codec Languages and DSLs Catalog dimension db id 225
Library dimension (catalog)
Roles linked in library: Video Codec Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Milvus (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Vector Database Tool
- Vendor
- Milvus Community
- License
- apache_2
- Year introduced
- 2019
- Confidence
- 0.80
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Milvus appears in a growing number of AI/vector-search job postings and is backed by active GitHub adoption, but it is still far less universal than PostgreSQL or Redis in general backend JDs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 1017
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Vector Databases Catalog dimension db id 198
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Vector Databases
vector-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Aliases — catalog
- Hugging Face (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Ai Model Hub Platform
- Vendor
- Hugging Face
- License
- apache_2
- Year introduced
- 2016
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Hugging Face appears in many ML/LLM job descriptions and has strong GitHub adoption; its Transformers ecosystem is a common default for model sharing and fine-tuning.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 899
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Elasticsearch (CANONICAL) primary
- ES (VERSION)
- ElasticSearch (VERSION)
- Elasticsearch 6 (VERSION)
- Elasticsearch 6.x (VERSION)
- Elasticsearch 7 (VERSION)
- Elasticsearch 7.x (VERSION)
- Elasticsearch 8 (VERSION)
- Elasticsearch 8.x (VERSION)
- Elasticsearch v6 (VERSION)
- Elasticsearch v7 (VERSION)
- Elasticsearch v8 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Search Datastore
- Vendor
- Elastic NV
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.93
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 8.x
Maturity reasoning: Commonly listed in job descriptions for search/log analytics roles and widely deployed in production; Elastic’s docs and ecosystem show sustained adoption rather than sunset or replacement.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2925
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Magento Search and Indexing Catalog dimension db id 403
Library dimension (catalog)
Roles linked in library: Magento Dev
-
Search and Content Discovery Catalog dimension db id 356
Library dimension (catalog)
Roles linked in library: Drupal Dev, Sitecore Dev
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Magento Search and Indexing
magento-search-and-indexing
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Search and Content Discovery
search-and-content-discovery
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| OpenAI API | in_db |
LLM Provider APIs
llm-provider-apis
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Milvus | in_db |
Vector Databases
vector-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hugging Face | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hugging Face | in_db |
React Frontend Development
d_init_01
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✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Elasticsearch | in_db |
Magento Search and Indexing
magento-search-and-indexing
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Elasticsearch | in_db |
Search and Content Discovery
search-and-content-discovery
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Open Source Models | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SPLADE V2 | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "IBM\u2019s greatest invention is the",
"last_5_words": "force for good in the world."
},
"text": "IBM\u2019s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world. Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we\u2019re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business. At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Now it\u2019s time for you to join us on our journey to being a responsible technology innovator and a force for good in the world.",
"word_count": 108
},
"certifications": [],
"company_name": "IBM",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"SaaS",
"Software Development"
],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science",
"raw": "Bachelor\u0027s or higher degree in Computer Science, Information Technology, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 3,
"raw": "Overall 3+ years of IT Industry experience"
},
"job_locations": [],
"role": "Vector Database Engineer",
"role_aliases": [
"Database Engineer",
"DB Engineer",
"Data Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Your Role and Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Software Engineering and Product development",
"last_5_words": "vector data structures."
},
"text": "Software Engineering and Product development is critical to the success of IBM and our clients worldwide. You will collaborate with top industry thinkers at IBM and utilise the newest software development tools, methodologies, and techniques to create solutions you can be proud of.\n\nWe are seeking seasoned people with a strong interest in data and AI to join our watsonx.data engineering team. We are seeking professionals who are knowledgeable about the latest offerings in data warehouse, data lake, and data lakehouse technologies.\n\nProfessionals who are already involved in these products\u0027 open source communities or who actively monitor them will have an edge during the hiring process.\n\nAs a Vector Database Engineer, you will play a crucial role in designing, implementing, and maintaining our vector database systems. You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data. The ideal candidate will have a strong background in database architecture, optimization, and a deep understanding of vector data structures.",
"word_count": 227
},
{
"bullet_count": 11,
"heading": "Required Technical and Professional Expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Overall 3+ years of IT",
"last_5_words": "communication and collaboration skills."
},
"text": "\u2022 Overall 3+ years of IT Industry experience\n\u2022 2+ years of with OpenAI Apis/Opensource models\n\u2022 Bachelor\u0027s or higher degree in Computer Science, Information Technology, or a related field.\n\u2022 Proven experience as a Database Engineer, with a focus on vector databases.\n\u2022 Strong proficiency in database design, optimization, and performance tuning.\n\u2022 Experience with vector data structures and algorithms.\n\u2022 Proficiency in programming languages such as Python, Java, or C++.\n\u2022 Knowledge of database technologies such as Mivus or similar DB.\n\u2022 Experience on hybrid search techniques such as SPLADE V2\n\u2022 Excellent problem-solving and analytical skills.\n\u2022 Strong communication and collaboration skills.",
"word_count": 109
},
{
"bullet_count": 3,
"heading": "Preferred Technical And Professional Expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 2+ years of with OpenAI",
"last_5_words": "with HuggingFace"
},
"text": "\u2022 2+ years of with OpenAI Apis/Opensource models\n\u2022 Familiarity with HuggingFace\n\u2022 Familiarity with Elasticsearch",
"word_count": 22
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "OpenAI API"
},
{
"is_primary": true,
"skill_name": "Open Source Models"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "Java"
},
{
"is_primary": true,
"skill_name": "C++"
},
{
"is_primary": true,
"skill_name": "Milvus"
},
{
"is_primary": true,
"skill_name": "SPLADE V2"
},
{
"is_primary": false,
"skill_name": "Hugging Face"
},
{
"is_primary": false,
"skill_name": "Elasticsearch"
}
],
"jd_role": {
"display_name": "Vector Database Engineer",
"rationale": null,
"role_aliases": [
"Database Engineer",
"DB Engineer",
"Data Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "IBM\u2019s greatest invention is the",
"last_5_words": "force for good in the world."
},
"text": "IBM\u2019s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world. Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we\u2019re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business. At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Now it\u2019s time for you to join us on our journey to being a responsible technology innovator and a force for good in the world.",
"word_count": 108
},
"certifications": [],
"company_name": "IBM",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"SaaS",
"Software Development"
],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science",
"raw": "Bachelor\u0027s or higher degree in Computer Science, Information Technology, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 3,
"raw": "Overall 3+ years of IT Industry experience"
},
"job_locations": [],
"role": "Vector Database Engineer",
"role_aliases": [
"Database Engineer",
"DB Engineer",
"Data Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Your Role and Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Software Engineering and Product development",
"last_5_words": "vector data structures."
},
"text": "Software Engineering and Product development is critical to the success of IBM and our clients worldwide. You will collaborate with top industry thinkers at IBM and utilise the newest software development tools, methodologies, and techniques to create solutions you can be proud of.\n\nWe are seeking seasoned people with a strong interest in data and AI to join our watsonx.data engineering team. We are seeking professionals who are knowledgeable about the latest offerings in data warehouse, data lake, and data lakehouse technologies.\n\nProfessionals who are already involved in these products\u0027 open source communities or who actively monitor them will have an edge during the hiring process.\n\nAs a Vector Database Engineer, you will play a crucial role in designing, implementing, and maintaining our vector database systems. You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data. The ideal candidate will have a strong background in database architecture, optimization, and a deep understanding of vector data structures.",
"word_count": 227
},
{
"bullet_count": 11,
"heading": "Required Technical and Professional Expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Overall 3+ years of IT",
"last_5_words": "communication and collaboration skills."
},
"text": "\u2022 Overall 3+ years of IT Industry experience\n\u2022 2+ years of with OpenAI Apis/Opensource models\n\u2022 Bachelor\u0027s or higher degree in Computer Science, Information Technology, or a related field.\n\u2022 Proven experience as a Database Engineer, with a focus on vector databases.\n\u2022 Strong proficiency in database design, optimization, and performance tuning.\n\u2022 Experience with vector data structures and algorithms.\n\u2022 Proficiency in programming languages such as Python, Java, or C++.\n\u2022 Knowledge of database technologies such as Mivus or similar DB.\n\u2022 Experience on hybrid search techniques such as SPLADE V2\n\u2022 Excellent problem-solving and analytical skills.\n\u2022 Strong communication and collaboration skills.",
"word_count": 109
},
{
"bullet_count": 3,
"heading": "Preferred Technical And Professional Expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 2+ years of with OpenAI",
"last_5_words": "with HuggingFace"
},
"text": "\u2022 2+ years of with OpenAI Apis/Opensource models\n\u2022 Familiarity with HuggingFace\n\u2022 Familiarity with Elasticsearch",
"word_count": 22
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "807865a4-800e-4baf-94b2-163ee8f95755",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Strong proficiency in database design, optimization, and performance tuning.",
"similarity": 0.5926
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Proven experience as a Database Engineer, with a focus on vector databases.",
"similarity": 0.4695
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "The ideal candidate will have a strong background in database architecture, optimization, and a deep understanding of vector data structures.",
"similarity": 0.4576
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.5066,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "We are seeking professionals who are knowledgeable about the latest offerings in data warehouse, data lake, and data lakehouse technologies.",
"similarity": 0.5171
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data.",
"similarity": 0.4822
},
{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
"sentence": "Strong proficiency in database design, optimization, and performance tuning.",
"similarity": 0.4788
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.4927,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
"sentence": "2+ years of with OpenAI Apis/Opensource models",
"similarity": 0.464
},
{
"kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
"sentence": "2+ years of with OpenAI Apis/Opensource models",
"similarity": 0.464
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data.",
"similarity": 0.4492
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4591,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "backend performance tuning",
"sentence": "Strong proficiency in database design, optimization, and performance tuning.",
"similarity": 0.5414
},
{
"kra_text": "service contract collaboration",
"sentence": "Strong communication and collaboration skills.",
"similarity": 0.439
},
{
"kra_text": "persistence and data modeling",
"sentence": "You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data.",
"similarity": 0.3684
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.4496,
"slug": "java-backend-developer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Strong proficiency in database design, optimization, and performance tuning.",
"similarity": 0.569
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "You will work closely with cross-functional teams, including software engineers and data scientists, to ensure efficient and high-performance storage and retrieval of vector data.",
"similarity": 0.3854
},
{
"kra_text": "Writes database access logic including SQL queries, ORM mappings, stored procedures, and migration scripts for relational databases like PostgreSQL and MySQL.",
"sentence": "Proven experience as a Database Engineer, with a focus on vector databases.",
"similarity": 0.3838
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.4461,
"slug": "backend-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Fullstack Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"C#",
"Java",
"Python"
],
"role_id": 435,
"score": 0.4286,
"slug": "fullstack-developer",
"total_count": 7
},
{
"display_name": "Fullstack Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"C#",
"Java",
"Python"
],
"role_id": 15,
"score": 0.4286,
"slug": "full-stack-engineer",
"total_count": 7
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"C#",
"Java",
"Python"
],
"role_id": 1,
"score": 0.4286,
"slug": "backend-engineer",
"total_count": 7
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"C#",
"Python"
],
"role_id": 3,
"score": 0.2857,
"slug": "ml-engineer",
"total_count": 7
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Java",
"Python"
],
"role_id": 2,
"score": 0.2857,
"slug": "data-engineer",
"total_count": 7
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "NoSQL DBA / Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 200,
"score": 0.91,
"slug": "nosql-dba-engineer",
"total_count": null
},
"confidence": 0.91,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Vector Database Engineering",
"Database Performance Optimization",
"Storage and Retrieval Systems",
"Hybrid Search Implementation",
"AI/ML Data Infrastructure",
"Cross-functional Collaboration"
],
"matched_kras": [
"designing, implementing, and maintaining our vector database systems",
"ensure efficient and high-performance storage and retrieval of vector data",
"strong background in database architecture, optimization, and performance tuning",
"work closely with cross-functional teams, including software engineers and data scientists",
"experience with vector data structures and algorithms"
],
"matched_skills": [
"OpenAI Apis",
"Opensource models",
"vector database systems",
"database architecture",
"database design",
"optimization",
"performance tuning",
"vector data structures",
"algorithms",
"Python",
"Java",
"C++",
"Mivus",
"SPLADE V2",
"HuggingFace",
"Elasticsearch"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Database Specialists; The JD focuses on designing, implementing, and optimizing vector database systems with technologies like Milvus, Elasticsearch, and hybrid search, which best matches a NoSQL / modern database engineering role.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 2,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 1588,
"r_and_r_preview": "Software Engineering and Product development is critical to the success of IBM and our clients worldwide. You will collaborate with top industry thinkers at IBM and utilise the newest software develop",
"role_display_name": "NoSQL DBA / Engineer",
"role_slug": "nosql-dba-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 21154,
"role_display_name": "NoSQL DBA / Engineer",
"role_slug": "nosql-dba-engineer",
"skill_name": "Open Source Models",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 21155,
"role_display_name": "NoSQL DBA / Engineer",
"role_slug": "nosql-dba-engineer",
"skill_name": "SPLADE V2",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1859,
"existing_alias_text": "OpenAI API",
"input_term": "OpenAI API",
"matched_canonical": {
"category_id": 11,
"display_name": "OpenAI API",
"id": 1223,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "openai-api",
"sub_category_id": 1006,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"id": 881,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "cpp17",
"alias_type": "VERSION",
"id": 882,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "cpp20",
"alias_type": "VERSION",
"id": 883,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "cpp23",
"alias_type": "VERSION",
"id": 884,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "cpp26",
"alias_type": "VERSION",
"id": 885,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "cpp98",
"alias_type": "VERSION",
"id": 886,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "csharp",
"alias_type": "VERSION",
"id": 4764,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "modern C++",
"alias_type": "VERSION",
"id": 428,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "C#",
"id": 4,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "c",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "C# and .NET Languages",
"id": 362,
"rationale": "Primary implementation languages and runtime features used to build backend service logic in the .NET ecosystem. This is the core coding surface for request handling, domain logic, and service internals.",
"slug": "c-and-net-languages",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cross-Platform App Languages",
"id": 167,
"rationale": "Languages used to implement shared mobile features across iOS and Android from a common codebase. This is the primary coding surface for hybrid app logic, UI behavior, and platform-specific branching.",
"slug": "cross-platform-app-languages",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Sitecore Development Languages",
"id": 438,
"rationale": "Core implementation languages and markup used to build Sitecore customizations, rendering logic, and site behavior. This is the primary authoring surface for Sitecore-specific code and templates.",
"slug": "sitecore-development-languages",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Sitecore Dev",
"id": 233,
"rationale": null,
"role_archetype": "Engineering",
"slug": "sitecore-dev",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Video Codec Languages and DSLs",
"id": 225,
"rationale": "Implementation languages and codec-specific description languages used to build compression, decoding, and transform logic. This is the primary coding surface for codec behavior and bitstream manipulation.",
"slug": "video-codec-languages-and-dsls",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Video Codec Engineer",
"id": 22,
"rationale": null,
"role_archetype": null,
"slug": "video-codec-engineer",
"source": "db"
}
]
}
],
"input_skill": "C++",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Milvus",
"alias_type": "CANONICAL",
"id": 1881,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Milvus",
"id": 1245,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "milvus",
"sub_category_id": 1017,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vector Databases",
"id": 198,
"rationale": "Specialized storage and indexing systems used to persist embeddings and support similarity search. This is a distinct vendor-family cluster because AI features often depend on a concrete vector store choice and its operational behavior.",
"slug": "vector-databases",
"source": "db"
},
"input_skill": "Milvus",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "Milvus",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "SPLADE V2",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "SHORT_LIVED",
"version_strategy": "VERSIONED",
"volatility": "FAST"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "splade-v2",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Hugging Face",
"alias_type": "CANONICAL",
"id": 1825,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Hugging Face",
"id": 1189,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "hugging-face",
"sub_category_id": 899,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ML Frameworks and Libraries",
"id": 40,
"rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
"slug": "ml-frameworks-and-libraries",
"source": "db"
},
"input_skill": "Hugging Face",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Hugging Face",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Hugging Face",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Elasticsearch",
"alias_type": "CANONICAL",
"id": 4648,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ES",
"alias_type": "VERSION",
"id": 4649,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ElasticSearch",
"alias_type": "VERSION",
"id": 4650,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 6",
"alias_type": "VERSION",
"id": 4651,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 6.x",
"alias_type": "VERSION",
"id": 4657,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 7",
"alias_type": "VERSION",
"id": 4652,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 7.x",
"alias_type": "VERSION",
"id": 4658,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 8",
"alias_type": "VERSION",
"id": 4653,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 8.x",
"alias_type": "VERSION",
"id": 4659,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v6",
"alias_type": "VERSION",
"id": 4654,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v7",
"alias_type": "VERSION",
"id": 4655,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v8",
"alias_type": "VERSION",
"id": 4656,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "Elasticsearch",
"id": 3171,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "elasticsearch",
"sub_category_id": 2925,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Magento Search and Indexing",
"id": 403,
"rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
"slug": "magento-search-and-indexing",
"source": "db"
},
"input_skill": "Elasticsearch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Magento Dev",
"id": 231,
"rationale": null,
"role_archetype": "Engineering",
"slug": "magento-dev",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Search and Content Discovery",
"id": 356,
"rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
"slug": "search-and-content-discovery",
"source": "db"
},
"input_skill": "Elasticsearch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"source": "db"
},
{
"display_name": "Sitecore Dev",
"id": 233,
"rationale": null,
"role_archetype": "Engineering",
"slug": "sitecore-dev",
"source": "db"
}
]
}
],
"input_skill": "Elasticsearch",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Open Source Models",
"SPLADE V2"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "NoSQL DBA / Engineer",
"id": 200,
"rationale": "Domain=Database Specialists; The JD focuses on designing, implementing, and optimizing vector database systems with technologies like Milvus, Elasticsearch, and hybrid search, which best matches a NoSQL / modern database engineering role.",
"role_archetype": null,
"slug": "nosql-dba-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "OpenAI API",
"tag": "in_db"
},
{
"skill": "Open Source Models",
"tag": "new"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Java",
"tag": "in_db"
},
{
"skill": "C++",
"tag": "in_db"
},
{
"skill": "Milvus",
"tag": "in_db"
},
{
"skill": "SPLADE V2",
"tag": "new"
},
{
"skill": "Hugging Face",
"tag": "in_db"
},
{
"skill": "Elasticsearch",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 200,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Provider APIs",
"id": 195,
"rationale": "Direct integration with hosted model APIs used to generate, classify, extract, and transform content. This is the primary vendor surface for shipping AI features because it determines model choice, request shape, streaming, tool use, and cost/latency tradeoffs.",
"slug": "llm-provider-apis",
"source": "db"
},
"dimension_id": 195,
"input_skill": "OpenAI API",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1223,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 200,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 200,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 200,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 200,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 200,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Python",
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